Optimum facility layout is of great importance for businesses seeking competitive advantage and sustainable operational performance, as layout decisions directly affect cost, time, efficiency, and productivity. However, determining an optimal facility layout is inherently complex due to the presence of multiple alternatives evaluated under conflicting criteria and the need to evaluate large numbers of candidate layouts and what-if scenarios. Relying on a single multi-criteria decision-making (MCDM) method, as commonly observed in the literature, may therefore lead to method-dependent or unstable conclusions. To address this limitation, this study proposes a robustness-oriented integrated group decision-making approach for facility layout selection that is designed to scale through parallel evaluation of method combinations and scenario batches. The approach combines two objective weighting methods—method based on the removal effects of criteria (MEREC) and statistical variance (SV)—with four conceptually distinct ranking methods, namely, combined compromise solution (COCOSO), complex proportional assessment (COPRAS), combinative distance-based assessment (CODAS), and multi-attributive border approximation area comparison (MABAC). Ranking consistency and divergence are systematically examined using Spearman’s correlation coefficient, and a consensus solution is obtained through the Copeland aggregation method. The applicability of the proposed approach is demonstrated using three facility layout problems. The numerical results show that ranking outcomes vary considerably across weighting–ranking combinations, with Spearman correlation coefficients ranging from strong agreement (rs > 0.95) to weak or negative association (rs as low as − 0.018), depending on the problem structure and aggregation logic. In the first problem, all method combinations consistently identify the same best alternative, indicating high robustness. In the second and third problems, notable rank reversals are observed, highlighting method- and weight-induced sensitivity. Additional sensitivity analyses based on extreme weight-dominance scenarios and method-specific parameter variation (CODAS threshold τ) demonstrate that while some methods exhibit stable top-ranked alternatives, others are more sensitive to changes. From a supercomputing perspective, the integrated pipeline naturally supports parallel or distributed execution across method pairs, alternatives, and scenario sets, enabling practical use in large-scale or near-real-time layout assessment settings. These findings confirm that the proposed integrated framework provides decision-relevant robustness insights that cannot be obtained from single-method analyses, thereby enhancing the reliability of facility layout selection.

An integrated multi-criteria decision-making approach for facility layout problems

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2026年3月3日

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Mehmet Şahin

摘要

Optimum facility layout is of great importance for businesses seeking competitive advantage and sustainable operational performance, as layout decisions directly affect cost, time, efficiency, and productivity. However, determining an optimal facility layout is inherently complex due to the presence of multiple alternatives evaluated under conflicting criteria and the need to evaluate large numbers of candidate layouts and what-if scenarios. Relying on a single multi-criteria decision-making (MCDM) method, as commonly observed in the literature, may therefore lead to method-dependent or unstable conclusions. To address this limitation, this study proposes a robustness-oriented integrated group decision-making approach for facility layout selection that is designed to scale through parallel evaluation of method combinations and scenario batches. The approach combines two objective weighting methods—method based on the removal effects of criteria (MEREC) and statistical variance (SV)—with four conceptually distinct ranking methods, namely, combined compromise solution (COCOSO), complex proportional assessment (COPRAS), combinative distance-based assessment (CODAS), and multi-attributive border approximation area comparison (MABAC). Ranking consistency and divergence are systematically examined using Spearman’s correlation coefficient, and a consensus solution is obtained through the Copeland aggregation method. The applicability of the proposed approach is demonstrated using three facility layout problems. The numerical results show that ranking outcomes vary considerably across weighting–ranking combinations, with Spearman correlation coefficients ranging from strong agreement (r_s > 0.95) to weak or negative association (r_s as low as − 0.018), depending on the problem structure and aggregation logic. In the first problem, all method combinations consistently identify the same best alternative, indicating high robustness. In the second and third problems, notable rank reversals are observed, highlighting method- and weight-induced sensitivity. Additional sensitivity analyses based on extreme weight-dominance scenarios and method-specific parameter variation (CODAS threshold τ) demonstrate that while some methods exhibit stable top-ranked alternatives, others are more sensitive to changes. From a supercomputing perspective, the integrated pipeline naturally supports parallel or distributed execution across method pairs, alternatives, and scenario sets, enabling practical use in large-scale or near-real-time layout assessment settings. These findings confirm that the proposed integrated framework provides decision-relevant robustness insights that cannot be obtained from single-method analyses, thereby enhancing the reliability of facility layout selection.